[{"type":"scientific_journal_article","user_id":"83781","intvolume":" 306","place":"Amsterdam","_id":"11808","doi":"10.1016/j.energy.2024.132318","article_number":"132318","publication_status":"published","department":[{"_id":"DEP6017"}],"abstract":[{"text":"The application of hydrogen for energy storage and as a vehicle fuel necessitates efficient and effective storage technologies. In addition to traditional cryogenic and high-pressure tanks, an alternative approach involves utilizing porous materials such as activated carbons within the storage tank. The adsorption behaviour of hydrogen in porous structures is described using the Dubinin-Astakhov isotherm. To model the flow of hydrogen within the tank, we rely on the equations of mass conservation, the Navier-Stokes equations, and the equation of energy conservation, which are implemented in a computational fluid dynamics code and additional terms account for the amount of hydrogen involved in sorption and the corresponding heat release. While physical models are valuable, data-driven models often offer computational advantages. Based on the data from the physical adsorption model, a data-driven model is derived using various machine learning techniques. This model is then incorporated as source terms in the governing conservation equations, resulting in a novel hybrid formulation which is computationally more efficient. Consequently, a new method is presented to compute the temperature and concentration distribution during the charging and discharging of hydrogen tanks and identifying any limiting phenomena more easily.","lang":"eng"}],"publisher":"Elsevier BV","publication":"Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy","publication_identifier":{"eissn":["1873-6785"],"issn":["0360-5442"]},"date_created":"2024-07-31T14:23:52Z","date_updated":"2024-08-01T08:16:04Z","keyword":["Hydrogen storage","Adsorption","Activated carbon","Machine learning","Simulation","Computational fluid dynamics"],"language":[{"iso":"eng"}],"title":"Modelling activated carbon hydrogen storage tanks using machine learning models","volume":306,"citation":{"short":"G.H. Klepp, Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024).","din1505-2-1":"Klepp, Georg Heinrich: Modelling activated carbon hydrogen storage tanks using machine learning models. In: Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy Bd. 306. Amsterdam, Elsevier BV (2024)","chicago-de":"Klepp, Georg Heinrich. 2024. Modelling activated carbon hydrogen storage tanks using machine learning models. Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy 306. doi:10.1016/j.energy.2024.132318, .","van":"Klepp GH. Modelling activated carbon hydrogen storage tanks using machine learning models. Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy. 2024;306.","ufg":"Klepp, Georg Heinrich: Modelling activated carbon hydrogen storage tanks using machine learning models, in: Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy 306 (2024).","mla":"Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks Using Machine Learning Models.” Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy, vol. 306, 132318, 2024, https://doi.org/10.1016/j.energy.2024.132318.","havard":"G.H. Klepp, Modelling activated carbon hydrogen storage tanks using machine learning models, Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy. 306 (2024).","bjps":"Klepp GH (2024) Modelling Activated Carbon Hydrogen Storage Tanks Using Machine Learning Models. Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy 306.","apa":"Klepp, G. H. (2024). Modelling activated carbon hydrogen storage tanks using machine learning models. Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy, 306, Article 132318. https://doi.org/10.1016/j.energy.2024.132318","chicago":"Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks Using Machine Learning Models.” Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024). https://doi.org/10.1016/j.energy.2024.132318.","ama":"Klepp GH. Modelling activated carbon hydrogen storage tanks using machine learning models. Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy. 2024;306. doi:10.1016/j.energy.2024.132318","ieee":"G. H. Klepp, “Modelling activated carbon hydrogen storage tanks using machine learning models,” Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy, vol. 306, Art. no. 132318, 2024, doi: 10.1016/j.energy.2024.132318."},"year":"2024","author":[{"full_name":"Klepp, Georg Heinrich","first_name":"Georg Heinrich","last_name":"Klepp","id":"49011"}],"status":"public"},{"publication_status":"published","article_type":"original","publisher":"MDPI AG","quality_controlled":"1","department":[{"_id":"DEP5023"},{"_id":"DEP5000"}],"abstract":[{"text":"Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.","lang":"eng"}],"issue":"4","user_id":"83781","intvolume":" 5","type":"scientific_journal_article","place":"Basel","_id":"12167","doi":"10.3390/eng5040145","volume":5,"citation":{"ieee":"R. Subramanian and U. Büker, “Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System,” Eng : advances in engineering, vol. 5, no. 4, pp. 2778–2804, 2024, doi: 10.3390/eng5040145.","ama":"Subramanian R, Büker U. Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System. Eng : advances in engineering. 2024;5(4):2778-2804. doi:10.3390/eng5040145","apa":"Subramanian, R., & Büker, U. (2024). Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System. Eng : Advances in Engineering, 5(4), 2778–2804. https://doi.org/10.3390/eng5040145","chicago":"Subramanian, Ramakrishnan, and Ulrich Büker. “Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System.” Eng : Advances in Engineering 5, no. 4 (2024): 2778–2804. https://doi.org/10.3390/eng5040145.","van":"Subramanian R, Büker U. Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System. Eng : advances in engineering. 2024;5(4):2778–804.","bjps":"Subramanian R and Büker U (2024) Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System. Eng : advances in engineering 5, 2778–2804.","havard":"R. Subramanian, U. Büker, Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System, Eng : Advances in Engineering. 5 (2024) 2778–2804.","mla":"Subramanian, Ramakrishnan, and Ulrich Büker. “Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System.” Eng : Advances in Engineering, vol. 5, no. 4, 2024, pp. 2778–804, https://doi.org/10.3390/eng5040145.","ufg":"Subramanian, Ramakrishnan/Büker, Ulrich: Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System, in: Eng : advances in engineering 5 (2024), H. 4, S. 2778–2804.","short":"R. Subramanian, U. Büker, Eng : Advances in Engineering 5 (2024) 2778–2804.","chicago-de":"Subramanian, Ramakrishnan und Ulrich Büker. 2024. Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System. Eng : advances in engineering 5, Nr. 4: 2778–2804. doi:10.3390/eng5040145, .","din1505-2-1":"Subramanian, Ramakrishnan ; Büker, Ulrich: Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System. In: Eng : advances in engineering Bd. 5. Basel, MDPI AG (2024), Nr. 4, S. 2778–2804"},"title":"Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System","status":"public","page":"2778-2804","author":[{"first_name":"Ramakrishnan","full_name":"Subramanian, Ramakrishnan","id":"85499","last_name":"Subramanian"},{"orcid":"0000-0002-4403-3889","last_name":"Büker","full_name":"Büker, Ulrich","id":"81453","first_name":"Ulrich"}],"year":"2024","date_updated":"2024-12-05T13:19:17Z","publication_identifier":{"eissn":["2673-4117"]},"publication":"Eng : advances in engineering","date_created":"2024-12-04T16:46:30Z","keyword":["autonomous vehicles","operational design domain","computer vision","machine learning","road surface detection"],"language":[{"iso":"eng"}]},{"quality_controlled":"1","publisher":"MDPI","department":[{"_id":"DEP4022"},{"_id":"DEP4028"},{"_id":"DEP4014"}],"issue":"8","abstract":[{"lang":"eng","text":"Wet granulation is a frequent process in the pharmaceutical industry. As a starting point for numerous dosage forms, the quality of the granulation not only affects subsequent production steps but also impacts the quality of the final product. It is thus crucial and economical to monitor this operation thoroughly. Here, we report on identifying different phases of a granulation process using a machine learning approach. The phases reflect the water content which, in turn, influences the processability and quality of the granule mass. We used two kinds of microphones and an acceleration sensor to capture acoustic emissions and vibrations. We trained convolutional neural networks (CNNs) to classify the different phases using transformed sound recordings as the input. We achieved a classification accuracy of up to 90% using vibrational data and an accuracy of up to 97% using the audible microphone data. Our results indicate the suitability of using audible sound and machine learning to monitor pharmaceutical processes. Moreover, since recording acoustic emissions is contactless, it readily complies with legal regulations and presents Good Manufacturing Practices."}],"publication_status":"published","isi":"1","place":"Basel","doi":"https://doi.org/10.3390/pharmaceutics15082153","article_number":"2153","_id":"10216","user_id":"83781","intvolume":" 15","type":"scientific_journal_article","pmid":"1","status":"public","author":[{"full_name":"Fulek, Ruwen","id":"79527","last_name":"Fulek","first_name":"Ruwen"},{"full_name":"Ramm, Selina","last_name":"Ramm","first_name":"Selina","id":"68713","orcid":"https://orcid.org/0000-0002-0502-8032"},{"first_name":"Christian","full_name":"Kiera, Christian","last_name":"Kiera"},{"orcid":"0000-0002-7920-0595","full_name":"Pein-Hackelbusch, Miriam","id":"64952","first_name":"Miriam","last_name":"Pein-Hackelbusch"},{"first_name":"Ulrich","last_name":"Odefey","full_name":"Odefey, Ulrich","id":"74218"}],"year":"2023","main_file_link":[{"open_access":"1","url":"https://www.mdpi.com/1999-4923/15/8/2153"}],"external_id":{"isi":["001119084200001"],"pmid":["37631367"]},"citation":{"short":"R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, U. Odefey, Pharmaceutics 15 (2023).","din1505-2-1":"Fulek, Ruwen ; Ramm, Selina ; Kiera, Christian ; Pein-Hackelbusch, Miriam ; Odefey, Ulrich: A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions. In: Pharmaceutics Bd. 15. Basel, MDPI (2023), Nr. 8","chicago-de":"Fulek, Ruwen, Selina Ramm, Christian Kiera, Miriam Pein-Hackelbusch und Ulrich Odefey. 2023. A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions. Pharmaceutics 15, Nr. 8. doi:https://doi.org/10.3390/pharmaceutics15082153, .","apa":"Fulek, R., Ramm, S., Kiera, C., Pein-Hackelbusch, M., & Odefey, U. (2023). A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions. Pharmaceutics, 15(8), Article 2153. https://doi.org/10.3390/pharmaceutics15082153","chicago":"Fulek, Ruwen, Selina Ramm, Christian Kiera, Miriam Pein-Hackelbusch, and Ulrich Odefey. “A Machine Learning Approach to Qualitatively Evaluate Different Granulation Phases by Acoustic Emissions.” Pharmaceutics 15, no. 8 (2023). https://doi.org/10.3390/pharmaceutics15082153.","van":"Fulek R, Ramm S, Kiera C, Pein-Hackelbusch M, Odefey U. A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions. Pharmaceutics. 2023;15(8).","ufg":"Fulek, Ruwen u. a.: A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions, in: Pharmaceutics 15 (2023), H. 8.","bjps":"Fulek R et al. (2023) A Machine Learning Approach to Qualitatively Evaluate Different Granulation Phases by Acoustic Emissions. Pharmaceutics 15.","havard":"R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, U. Odefey, A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions, Pharmaceutics. 15 (2023).","mla":"Fulek, Ruwen, et al. “A Machine Learning Approach to Qualitatively Evaluate Different Granulation Phases by Acoustic Emissions.” Pharmaceutics, vol. 15, no. 8, 2153, 2023, https://doi.org/10.3390/pharmaceutics15082153.","ieee":"R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, and U. Odefey, “A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions,” Pharmaceutics, vol. 15, no. 8, Art. no. 2153, 2023, doi: https://doi.org/10.3390/pharmaceutics15082153.","ama":"Fulek R, Ramm S, Kiera C, Pein-Hackelbusch M, Odefey U. A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions. Pharmaceutics. 2023;15(8). doi:https://doi.org/10.3390/pharmaceutics15082153"},"volume":15,"title":"A machine learning approach to qualitatively evaluate different granulation phases by acoustic emissions","language":[{"iso":"eng"}],"keyword":["wet granulation","acoustic classification","machine learning","convolutional neural networks"],"oa":"1","date_updated":"2025-07-29T13:21:40Z","date_created":"2023-08-15T10:48:15Z","publication":"Pharmaceutics","publication_identifier":{"eissn":["1999-4923 "]}},{"year":"2023","author":[{"id":"52317","first_name":"Paul","full_name":"Wunderlich, Paul","last_name":"Wunderlich"},{"last_name":"Wiegräbe","id":"76510","full_name":"Wiegräbe, Frauke","first_name":"Frauke"},{"last_name":"Dörksen","full_name":"Dörksen, Helene","first_name":"Helene","id":"46416"}],"external_id":{"isi":["000918039900001"],"pmid":["36673969"]},"status":"public","pmid":"1","title":"Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance","citation":{"apa":"Wunderlich, P., Wiegräbe, F., & Dörksen, H. (2023). Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 20(2), Article 1215. https://doi.org/10.3390/ijerph20021215","chicago":"Wunderlich, Paul, Frauke Wiegräbe, and Helene Dörksen. “Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance.” INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20, no. 2 (2023). https://doi.org/10.3390/ijerph20021215.","van":"Wunderlich P, Wiegräbe F, Dörksen H. Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2023;20(2).","mla":"Wunderlich, Paul, et al. “Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance.” INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, vol. 20, no. 2, 1215, 2023, https://doi.org/10.3390/ijerph20021215.","bjps":"Wunderlich P, Wiegräbe F and Dörksen H (2023) Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20.","havard":"P. Wunderlich, F. Wiegräbe, H. Dörksen, Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 20 (2023).","ufg":"Wunderlich, Paul/Wiegräbe, Frauke/Dörksen, Helene: Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance, in: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20 (2023), H. 2.","short":"P. Wunderlich, F. Wiegräbe, H. Dörksen, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20 (2023).","chicago-de":"Wunderlich, Paul, Frauke Wiegräbe und Helene Dörksen. 2023. Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 20, Nr. 2. doi:10.3390/ijerph20021215, .","din1505-2-1":"Wunderlich, Paul ; Wiegräbe, Frauke ; Dörksen, Helene: Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. In: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH Bd. 20. Basel, MDPI (2023), Nr. 2","ieee":"P. Wunderlich, F. Wiegräbe, and H. Dörksen, “Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance,” INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, vol. 20, no. 2, Art. no. 1215, 2023, doi: 10.3390/ijerph20021215.","ama":"Wunderlich P, Wiegräbe F, Dörksen H. Digital Case Manager-A Data-Driven Tool to Support Family Caregivers with Initial Guidance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2023;20(2). doi:10.3390/ijerph20021215"},"volume":20,"keyword":["machine learning","healthcare","case management","caring","multi-label classification"],"language":[{"iso":"eng"}],"publication":"INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH","publication_identifier":{"issn":["1661-7827 "],"eissn":["1660-4601"]},"date_created":"2025-04-14T13:39:52Z","date_updated":"2025-06-25T13:11:41Z","department":[{"_id":"DEP5023"},{"_id":"DEP5000"}],"issue":"2","abstract":[{"lang":"eng","text":"Due to the demographic aging of society, the demand for skilled caregiving is increasing. However, the already existing shortage of professional caregivers will exacerbate in the future. As a result, family caregivers must shoulder a heavier share of the care burden. To ease the burden and promote a better work-life balance, we developed the Digital Case Manager. This tool uses machine learning algorithms to learn the relationship between a care situation and the next care steps and helps family caregivers balance their professional and private lives so that they are able to continue caring for their family members without sacrificing their own jobs and personal ambitions. The data for the machine learning model are generated by means of a questionnaire based on professional assessment instruments. We implemented a proof-of-concept of the Digital Case Manager and initial tests show promising results. It offers a quick and easy-to-use tool for family caregivers in the early stages of a care situation."}],"publisher":"MDPI","quality_controlled":"1","isi":"1","publication_status":"published","place":"Basel","_id":"12785","doi":"10.3390/ijerph20021215","article_number":"1215","type":"scientific_journal_article","user_id":"83781","intvolume":" 20"},{"language":[{"iso":"eng"}],"keyword":["machine learning","online algorithms","cyber-physical production systems","surrogate-based optimization"],"date_created":"2025-04-16T07:27:52Z","publication":" Applied Sciences : open access journal","publication_identifier":{"issn":["2076-3417"]},"date_updated":"2025-06-26T07:50:56Z","author":[{"first_name":"Alexander","last_name":"Hinterleitner","full_name":"Hinterleitner, Alexander"},{"first_name":"Richard","last_name":"Schulz","full_name":"Schulz, Richard"},{"first_name":"Lukas","last_name":"Hans","full_name":"Hans, Lukas"},{"first_name":"Aleksandr","last_name":"Subbotin","full_name":"Subbotin, Aleksandr"},{"last_name":"Barthel","first_name":"Nils","full_name":"Barthel, Nils"},{"full_name":"Pütz, Noah","first_name":"Noah","last_name":"Pütz"},{"full_name":"Rosellen, Martin","first_name":"Martin","last_name":"Rosellen"},{"first_name":"Thomas","full_name":"Bartz-Beielstein, Thomas","last_name":"Bartz-Beielstein"},{"first_name":"Christoph","full_name":"Geng, Christoph","last_name":"Geng","id":"61408"},{"full_name":"Priss, Phillip","last_name":"Priss","first_name":"Phillip"}],"year":"2023","external_id":{"isi":["001096019200001"]},"status":"public","title":"Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture","volume":13,"citation":{"ama":"Hinterleitner A, Schulz R, Hans L, et al. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Applied Sciences : open access journal. 2023;13(20). doi:10.3390/app132011506","ieee":"A. Hinterleitner et al., “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture,” Applied Sciences : open access journal, vol. 13, no. 20, Art. no. 11506, 2023, doi: 10.3390/app132011506.","ufg":"Hinterleitner, Alexander u. a.: Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture, in: Applied Sciences : open access journal 13 (2023), H. 20.","havard":"A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz, M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture, Applied Sciences : Open Access Journal. 13 (2023).","mla":"Hinterleitner, Alexander, et al. “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.” Applied Sciences : Open Access Journal, vol. 13, no. 20, 11506, 2023, https://doi.org/10.3390/app132011506.","bjps":"Hinterleitner A et al. (2023) Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Applied Sciences : open access journal 13.","van":"Hinterleitner A, Schulz R, Hans L, Subbotin A, Barthel N, Pütz N, et al. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Applied Sciences : open access journal. 2023;13(20).","chicago":"Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng, and Phillip Priss. “Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture.” Applied Sciences : Open Access Journal 13, no. 20 (2023). https://doi.org/10.3390/app132011506.","apa":"Hinterleitner, A., Schulz, R., Hans, L., Subbotin, A., Barthel, N., Pütz, N., Rosellen, M., Bartz-Beielstein, T., Geng, C., & Priss, P. (2023). Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Applied Sciences : Open Access Journal, 13(20), Article 11506. https://doi.org/10.3390/app132011506","din1505-2-1":"Hinterleitner, Alexander ; Schulz, Richard ; Hans, Lukas ; Subbotin, Aleksandr ; Barthel, Nils ; Pütz, Noah ; Rosellen, Martin ; Bartz-Beielstein, Thomas ; u. a.: Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. In: Applied Sciences : open access journal Bd. 13. Basel, MDPI AG (2023), Nr. 20","chicago-de":"Hinterleitner, Alexander, Richard Schulz, Lukas Hans, Aleksandr Subbotin, Nils Barthel, Noah Pütz, Martin Rosellen, Thomas Bartz-Beielstein, Christoph Geng und Phillip Priss. 2023. Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture. Applied Sciences : open access journal 13, Nr. 20. doi:10.3390/app132011506, .","short":"A. Hinterleitner, R. Schulz, L. Hans, A. Subbotin, N. Barthel, N. Pütz, M. Rosellen, T. Bartz-Beielstein, C. Geng, P. Priss, Applied Sciences : Open Access Journal 13 (2023)."},"place":"Basel","article_number":"11506","doi":"10.3390/app132011506","_id":"12806","type":"scientific_journal_article","user_id":"83781","intvolume":" 13","department":[{"_id":"DEP5023"}],"abstract":[{"text":"Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.","lang":"eng"}],"issue":"20","publisher":"MDPI AG","isi":"1","publication_status":"published"},{"doi":"https://doi.org/10.23763/BrSc21-10wefing","_id":"6689","type":"journal_article","user_id":"83781","intvolume":" 74","department":[{"_id":"DEP1308"},{"_id":"DEP4028"}],"issue":"9/10","abstract":[{"lang":"eng","text":"Free amino nitrogen (FAN) concentrations in beer mash can be determined with machine learning algorithms\r\nfrom near-infrared (NIR) spectra. NIR spectroscopy is an alternative to a classical chemical analysis and\r\nallows for the application of inline process quality control. This study investigates the capabilities of\r\ndifferent machine learning techniques such as Ordinary Least Squares (OLS) regression, Decision Tree\r\nRegressor (DTR), Bayesian Ridge Regression (BRR), Ridge Regression (RR), K-nearest neighbours (KNN)\r\nregression as well as Support Vector Regression (SVR) to predict the FAN content in beer mash from NIR\r\nspectra. Various pre-processing strategies such as principal component analysis (PCA) and data\r\nstandardization were used to process NIR data that were used to train the machine learning algorithms.\r\nAlgorithm training was conducted with NIR data obtained from 16 beer mashes with varying FAN\r\nconcentrations. The trained models were then validated with 4 beer mashes that were not used for model\r\ntraining. Machine learning algorithms based on linear regression showed the highest prediction accuracy on\r\nunpre-processed data. BRR reached a root mean square error of calibration (RMSEC) of 2.58 mg/L (R2 = 0.96)\r\nand a prediction accuracy (RMSEP) of 2.81 mg/L (R2 = 0.96). The FAN concentration range of the investigated\r\nsamples was between approx. 180 and 220 mg/L. Machine learning based NIR spectra analysis is an alternative\r\nto classical chemical FAN level determination methods and can also be used as inline sensor system."}],"quality_controlled":"1","publisher":"Carl","publication_status":"published","article_type":"original","oa":"1","language":[{"iso":"eng"}],"keyword":["mashing","NIR","machine learning","FAN"],"date_created":"2021-11-02T10:06:04Z","publication_identifier":{"issn":["1866-5195"],"eissn":["0723-1520"]},"publication":"Brewing science ","date_updated":"2025-01-30T15:43:53Z","author":[{"first_name":"Patrick","last_name":"Wefing","id":"68976","full_name":"Wefing, Patrick"},{"last_name":"Conradi","full_name":"Conradi, Florian","id":"68967","first_name":"Florian"},{"first_name":"Johannes","full_name":"Rämisch, Johannes","last_name":"Rämisch"},{"full_name":"Neubauer, Peter","last_name":"Neubauer","first_name":"Peter"},{"id":"13209","first_name":"Jan","full_name":"Schneider, Jan","last_name":"Schneider","orcid":"0000-0001-6401-8873"}],"year":"2021","main_file_link":[{"url":"https://www.researchgate.net/publication/355735532_Determination_of_free_amino_nitrogen_in_beer_mash_with_an_inline_NIR_transflectance_probe_and_data_evaluation_by_machine_learning_algorithms","open_access":"1"}],"page":"107 - 121","status":"public","title":"Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms","volume":74,"citation":{"ieee":"P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, and J. Schneider, “Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms,” Brewing science , vol. 74, no. 9/10, pp. 107–121, 2021, doi: https://doi.org/10.23763/BrSc21-10wefing.","ama":"Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. Brewing science . 2021;74(9/10):107-121. doi:https://doi.org/10.23763/BrSc21-10wefing","short":"P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Brewing Science 74 (2021) 107–121.","chicago-de":"Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer und Jan Schneider. 2021. Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. Brewing science 74, Nr. 9/10: 107–121. doi:https://doi.org/10.23763/BrSc21-10wefing, .","din1505-2-1":"Wefing, Patrick ; Conradi, Florian ; Rämisch, Johannes ; Neubauer, Peter ; Schneider, Jan: Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. In: Brewing science Bd. 74, Carl (2021), Nr. 9/10, S. 107–121","apa":"Wefing, P., Conradi, F., Rämisch, J., Neubauer, P., & Schneider, J. (2021). Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. Brewing Science , 74(9/10), 107–121. https://doi.org/10.23763/BrSc21-10wefing","chicago":"Wefing, Patrick, Florian Conradi, Johannes Rämisch, Peter Neubauer, and Jan Schneider. “Determination of Free Amino Nitrogen in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms.” Brewing Science 74, no. 9/10 (2021): 107–21. https://doi.org/10.23763/BrSc21-10wefing.","van":"Wefing P, Conradi F, Rämisch J, Neubauer P, Schneider J. Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms. Brewing science . 2021;74(9/10):107–21.","bjps":"Wefing P et al. (2021) Determination of Free Amino Nitrogen in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms. Brewing science 74, 107–121.","mla":"Wefing, Patrick, et al. “Determination of Free Amino Nitrogen in Beer Mash with an Inline NIR Transflectance Probe and Data Evaluation by Machine Learning Algorithms.” Brewing Science , vol. 74, no. 9/10, 2021, pp. 107–21, https://doi.org/10.23763/BrSc21-10wefing.","havard":"P. Wefing, F. Conradi, J. Rämisch, P. Neubauer, J. Schneider, Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms, Brewing Science . 74 (2021) 107–121.","ufg":"Wefing, Patrick u. a.: Determination of free amino nitrogen in beer mash with an inline NIR transflectance probe and data evaluation by machine learning algorithms, in: Brewing science 74 (2021), H. 9/10, S. 107–121."}},{"keyword":["Cognition","Industry 40","Big data platform","Machine learning","CPPS","Optimization","Algorithm selection","Simulation"],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0268-3768"],"eissn":["1433-3015"]},"publication":"The International Journal of Advanced Manufacturing Technology","date_created":"2025-04-15T13:05:17Z","date_updated":"2025-06-26T13:39:22Z","author":[{"first_name":"Jan","last_name":"Strohschein","full_name":"Strohschein, Jan"},{"full_name":"Fischbach, Andreas","first_name":"Andreas","last_name":"Fischbach"},{"last_name":"Bunte","id":"58885","full_name":"Bunte, Andreas","first_name":"Andreas"},{"first_name":"Heide","full_name":"Faeskorn-Woyke, Heide","last_name":"Faeskorn-Woyke"},{"id":"44238","first_name":"Natalia","full_name":"Moriz, Natalia","last_name":"Moriz"},{"last_name":"Bartz-Beielstein","first_name":"Thomas","full_name":"Bartz-Beielstein, Thomas"}],"year":"2021","external_id":{"isi":["000659025000010"]},"status":"public","page":"3513-3532","title":"Cognitive capabilities for the CAAI in cyber-physical production systems","citation":{"ama":"Strohschein J, Fischbach A, Bunte A, Faeskorn-Woyke H, Moriz N, Bartz-Beielstein T. Cognitive capabilities for the CAAI in cyber-physical production systems. The International Journal of Advanced Manufacturing Technology. 2021;115(11-12):3513-3532. doi:10.1007/s00170-021-07248-3","ieee":"J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, and T. Bartz-Beielstein, “Cognitive capabilities for the CAAI in cyber-physical production systems,” The International Journal of Advanced Manufacturing Technology, vol. 115, no. 11–12, pp. 3513–3532, 2021, doi: 10.1007/s00170-021-07248-3.","short":"J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, T. Bartz-Beielstein, The International Journal of Advanced Manufacturing Technology 115 (2021) 3513–3532.","chicago-de":"Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz und Thomas Bartz-Beielstein. 2021. Cognitive capabilities for the CAAI in cyber-physical production systems. The International Journal of Advanced Manufacturing Technology 115, Nr. 11–12: 3513–3532. doi:10.1007/s00170-021-07248-3, .","din1505-2-1":"Strohschein, Jan ; Fischbach, Andreas ; Bunte, Andreas ; Faeskorn-Woyke, Heide ; Moriz, Natalia ; Bartz-Beielstein, Thomas: Cognitive capabilities for the CAAI in cyber-physical production systems. In: The International Journal of Advanced Manufacturing Technology Bd. 115. London [u.a.], Springer (2021), Nr. 11–12, S. 3513–3532","van":"Strohschein J, Fischbach A, Bunte A, Faeskorn-Woyke H, Moriz N, Bartz-Beielstein T. Cognitive capabilities for the CAAI in cyber-physical production systems. The International Journal of Advanced Manufacturing Technology. 2021;115(11–12):3513–32.","ufg":"Strohschein, Jan u. a.: Cognitive capabilities for the CAAI in cyber-physical production systems, in: The International Journal of Advanced Manufacturing Technology 115 (2021), H. 11–12, S. 3513–3532.","mla":"Strohschein, Jan, et al. “Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems.” The International Journal of Advanced Manufacturing Technology, vol. 115, no. 11–12, 2021, pp. 3513–32, https://doi.org/10.1007/s00170-021-07248-3.","bjps":"Strohschein J et al. (2021) Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems. The International Journal of Advanced Manufacturing Technology 115, 3513–3532.","havard":"J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, T. Bartz-Beielstein, Cognitive capabilities for the CAAI in cyber-physical production systems, The International Journal of Advanced Manufacturing Technology. 115 (2021) 3513–3532.","apa":"Strohschein, J., Fischbach, A., Bunte, A., Faeskorn-Woyke, H., Moriz, N., & Bartz-Beielstein, T. (2021). Cognitive capabilities for the CAAI in cyber-physical production systems. The International Journal of Advanced Manufacturing Technology, 115(11–12), 3513–3532. https://doi.org/10.1007/s00170-021-07248-3","chicago":"Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. “Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems.” The International Journal of Advanced Manufacturing Technology 115, no. 11–12 (2021): 3513–32. https://doi.org/10.1007/s00170-021-07248-3."},"volume":115,"place":"London [u.a.]","_id":"12800","doi":"10.1007/s00170-021-07248-3","type":"scientific_journal_article","user_id":"83781","intvolume":" 115","department":[{"_id":"DEP5023"}],"issue":"11-12","abstract":[{"lang":"eng","text":"his paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case."}],"publisher":"Springer ","isi":"1","publication_status":"published"},{"volume":12203,"citation":{"apa":"Besginow, A., Büttner, S., & Röcker, C. (2020). Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. 22nd International Conference on Human-Computer Interaction, 12203, 178–192. https://doi.org/10.1007/978-3-030-50344-4_14","chicago":"Besginow, Andreas, Sebastian Büttner, and Carsten Röcker. “Making Object Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data Generation.” In 22nd International Conference on Human-Computer Interaction, 12203:178–92. Lecture Notes in Computer Science . Berlin: Springer, 2020. https://doi.org/10.1007/978-3-030-50344-4_14.","van":"Besginow A, Büttner S, Röcker C. Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction. Berlin: Springer; 2020. p. 178–92. (Lecture Notes in Computer Science ; vol. 12203).","mla":"Besginow, Andreas, et al. “Making Object Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data Generation.” 22nd International Conference on Human-Computer Interaction, vol. 12203, Springer, 2020, pp. 178–92, https://doi.org/10.1007/978-3-030-50344-4_14.","havard":"A. Besginow, S. Büttner, C. Röcker, Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation, in: 22nd International Conference on Human-Computer Interaction, Springer, Berlin, 2020: pp. 178–192.","bjps":"Besginow A, Büttner S and Röcker C (2020) Making Object Detection Available to Everyone - A Hardware Prototype for Semi-Automatic Synthetic Data Generation. 22nd International Conference on Human-Computer Interaction, vol. 12203. Berlin: Springer, pp. 178–192.","ufg":"Besginow, Andreas/Büttner, Sebastian/Röcker, Carsten: Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation, in: o. Hg.: 22nd International Conference on Human-Computer Interaction, Bd. 12203, Berlin 2020 (Lecture Notes in Computer Science ), S. 178–192.","short":"A. Besginow, S. Büttner, C. Röcker, in: 22nd International Conference on Human-Computer Interaction, Springer, Berlin, 2020, pp. 178–192.","chicago-de":"Besginow, Andreas, Sebastian Büttner und Carsten Röcker. 2020. Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction, 12203:178–192. Lecture Notes in Computer Science . Berlin: Springer. doi:https://doi.org/10.1007/978-3-030-50344-4_14, .","din1505-2-1":"Besginow, Andreas ; Büttner, Sebastian ; Röcker, Carsten: Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction, Lecture Notes in Computer Science . Bd. 12203. Berlin : Springer, 2020, S. 178–192","ieee":"A. Besginow, S. Büttner, and C. Röcker, “Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation,” in 22nd International Conference on Human-Computer Interaction, Copenhagen, Denmark, 2020, vol. 12203, pp. 178–192. doi: https://doi.org/10.1007/978-3-030-50344-4_14.","ama":"Besginow A, Büttner S, Röcker C. Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation. In: 22nd International Conference on Human-Computer Interaction. Vol 12203. Lecture Notes in Computer Science . Springer; 2020:178-192. doi:https://doi.org/10.1007/978-3-030-50344-4_14"},"title":"Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation","status":"public","page":"178-192","year":"2020","main_file_link":[{"open_access":"1","url":"https://link.springer.com/chapter/10.1007/978-3-030-50344-4_14"}],"author":[{"first_name":"Andreas","full_name":"Besginow, Andreas","last_name":"Besginow","id":"61743"},{"last_name":"Büttner","first_name":"Sebastian","id":"61868","full_name":"Büttner, Sebastian"},{"id":"61525","first_name":"Carsten","full_name":"Röcker, Carsten","last_name":"Röcker"}],"date_updated":"2025-06-26T13:28:35Z","publication":"22nd International Conference on Human-Computer Interaction","publication_identifier":{"eisbn":["978-3-030-50344-4"],"isbn":["978-3-030-50343-7"]},"date_created":"2020-11-26T14:10:04Z","keyword":["Object detection","Synthetic datasets","Machine learning","Deep learning"],"language":[{"iso":"eng"}],"oa":"1","publication_status":"published","series_title":"Lecture Notes in Computer Science ","publisher":"Springer","conference":{"location":"Copenhagen, Denmark","name":"22nd International Conference on Human-Computer Interaction","end_date":"2020-07-24","start_date":"2020-07-19"},"department":[{"_id":"DEP5023"}],"abstract":[{"lang":"eng","text":"The capabilities of object detection are well known, but many projects don’t use them, despite potential benefit. Even though the use of object detection algorithms is facilitated through frameworks and publications, a big issue is the creation of the necessary training data. To tackle this issue, this work shows the design and evaluation of a prototype, which allows users to create synthetic datasets for object detection in images. The prototype is evaluated using YOLOv3 as the underlying detector and shows that the generated datasets are equally good in quality as manually created data. This encourages a wide adoption of object detection algorithms in different areas, since image creation and labeling is often the most time consuming step."}],"user_id":"83781","intvolume":" 12203","type":"conference","place":"Berlin","_id":"4097","doi":"https://doi.org/10.1007/978-3-030-50344-4_14"},{"type":"conference_editor_article","user_id":"83781","doi":"10.5220/0010130300870092","_id":"12812","publication_status":"published","abstract":[{"text":"Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.","lang":"eng"}],"department":[{"_id":"DEP5000"}],"conference":{"end_date":"2020-11-04","start_date":"2020-11-02","name":"International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)","location":"Budapest, HUNGARY"},"publisher":"SCITEPRESS - Science and Technology Publications","date_created":"2025-04-17T06:20:07Z","publication_identifier":{"isbn":["978-989-758-476-3"]},"publication":" Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1","editor":[{"full_name":"Panetto, H.","last_name":"Panetto","first_name":"H."},{"first_name":"K.","full_name":"Madani, K.","last_name":"Madani"},{"full_name":"Smirnov, A.","first_name":"A.","last_name":"Smirnov"}],"date_updated":"2025-06-26T13:31:38Z","language":[{"iso":"eng"}],"keyword":["Anomaly Detection","Gaussian Processes","Explainable Machine Learning","Industry 4.0"],"title":"Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0","citation":{"chicago-de":"Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. Hg. von H. Panetto, K. Madani, und A. Smirnov. Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications. doi:10.5220/0010130300870092, .","din1505-2-1":"Berns, Fabian ; Lange-Hegermann, Markus ; Beecks, Christian ; Panetto, H. ; Madani, K. ; Smirnov, A. (Hrsg.): Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0 : SCITEPRESS - Science and Technology Publications, 2020","short":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","chicago":"Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. Edited by H. Panetto, K. Madani, and A. Smirnov. Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications, 2020. https://doi.org/10.5220/0010130300870092.","apa":"Berns, F., Lange-Hegermann, M., & Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto, K. Madani, & A. Smirnov (Eds.), Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1 (pp. 87–92). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010130300870092","ufg":"Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg. von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.","havard":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","bjps":"Berns F, Lange-Hegermann M and Beecks C (2020) Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.","mla":"Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0.” Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1, edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92, https://doi.org/10.5220/0010130300870092.","van":"Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov A, editors. Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications; 2020.","ieee":"F. Berns, M. Lange-Hegermann, and C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92. doi: 10.5220/0010130300870092.","ama":"Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. (Panetto H, Madani K, Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92. doi:10.5220/0010130300870092"},"year":"2020","author":[{"first_name":"Fabian","last_name":"Berns","full_name":"Berns, Fabian"},{"first_name":"Markus","full_name":"Lange-Hegermann, Markus","last_name":"Lange-Hegermann","id":"71761"},{"last_name":"Beecks","first_name":"Christian","full_name":"Beecks, Christian"}],"page":"87-92","status":"public"},{"publication_status":"published","department":[{"_id":"DEP5023"},{"_id":"DEP5019"}],"conference":{"name":"14th IEEE International Workshop on Factory Communication Systems (WFCS)","location":"Imperia, Italy ","end_date":"2018-06-15","start_date":"2018-06-13"},"abstract":[{"lang":"eng","text":"In ever changing world, the industrial systems become more and more complex. Machine feedback in the form of alarms and notifications, due to its growing volume, becomes overwhelming for the operator. In addition, expectations in relation to system availability are growing as well. Therefore, there exists strong need for new solutions guaranteeing fast troubleshooting of problems that arise during system operation. The approach proposed in this study uses advantages of the Asset Administration Shell, machine learning, and human-machine interaction in order to create the assistance system which holistically addresses the issue of troubleshooting complex industrial systems."}],"publisher":"IEEE","type":"conference","user_id":"45673","place":"Piscataway, NJ","_id":"4327","doi":"10.1109/WFCS.2018.8402380","title":"Assistance System to Support Troubleshooting of Complex Industrial Systems","citation":{"ufg":"Lang, Dorota et. al. (2018): Assistance System to Support Troubleshooting of Complex Industrial Systems, in: 14th IEEE International Workshop on Factory Communication Systems (WFCS), Piscataway, NJ.","havard":"D. Lang, P. Wunderlich, M. Heinz, L. Wisniewski, J. Jasperneite, O. Niggemann, C. Röcker, Assistance System to Support Troubleshooting of Complex Industrial Systems, in: 14th IEEE International Workshop on Factory Communication Systems (WFCS), IEEE, Piscataway, NJ, 2018.","mla":"Lang, Dorota, et al. “Assistance System to Support Troubleshooting of Complex Industrial Systems.” 14th IEEE International Workshop on Factory Communication Systems (WFCS), IEEE, 2018, doi:10.1109/WFCS.2018.8402380.","bjps":"Lang D et al. (2018) Assistance System to Support Troubleshooting of Complex Industrial Systems. 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE.","van":"Lang D, Wunderlich P, Heinz M, Wisniewski L, Jasperneite J, Niggemann O, et al. Assistance System to Support Troubleshooting of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE; 2018.","ama":"Lang D, Wunderlich P, Heinz M, et al. Assistance System to Support Troubleshooting of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE; 2018. doi:10.1109/WFCS.2018.8402380","chicago":"Lang, Dorota, Paul Wunderlich, Mario Heinz, Lukasz Wisniewski, Jürgen Jasperneite, Oliver Niggemann, and Carsten Röcker. “Assistance System to Support Troubleshooting of Complex Industrial Systems.” In 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE, 2018. https://doi.org/10.1109/WFCS.2018.8402380.","apa":"Lang, D., Wunderlich, P., Heinz, M., Wisniewski, L., Jasperneite, J., Niggemann, O., & Röcker, C. (2018). Assistance System to Support Troubleshooting of Complex Industrial Systems. In 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE. https://doi.org/10.1109/WFCS.2018.8402380","ieee":"D. Lang et al., “Assistance System to Support Troubleshooting of Complex Industrial Systems,” in 14th IEEE International Workshop on Factory Communication Systems (WFCS), Imperia, Italy , 2018.","chicago-de":"Lang, Dorota, Paul Wunderlich, Mario Heinz, Lukasz Wisniewski, Jürgen Jasperneite, Oliver Niggemann und Carsten Röcker. 2018. Assistance System to Support Troubleshooting of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ: IEEE. doi:10.1109/WFCS.2018.8402380, .","din1505-2-1":"Lang, Dorota ; Wunderlich, Paul ; Heinz, Mario ; Wisniewski, Lukasz ; Jasperneite, Jürgen ; Niggemann, Oliver ; Röcker, Carsten: Assistance System to Support Troubleshooting of Complex Industrial Systems. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS). Piscataway, NJ : IEEE, 2018","short":"D. Lang, P. Wunderlich, M. Heinz, L. Wisniewski, J. Jasperneite, O. Niggemann, C. Röcker, in: 14th IEEE International Workshop on Factory Communication Systems (WFCS), IEEE, Piscataway, NJ, 2018."},"author":[{"full_name":"Lang, Dorota","first_name":"Dorota","id":"68941","last_name":"Lang"},{"id":"52317","last_name":"Wunderlich","full_name":"Wunderlich, Paul","first_name":"Paul"},{"last_name":"Heinz","id":"68913","full_name":"Heinz, Mario","first_name":"Mario"},{"full_name":"Wisniewski, Lukasz","last_name":"Wisniewski","id":"1710","first_name":"Lukasz"},{"first_name":"Jürgen","last_name":"Jasperneite","id":"1899","full_name":"Jasperneite, Jürgen"},{"last_name":"Niggemann","first_name":"Oliver","id":"10876","full_name":"Niggemann, Oliver"},{"last_name":"Röcker","full_name":"Röcker, Carsten","first_name":"Carsten","id":"61525"}],"main_file_link":[{"open_access":"1"}],"year":2018,"status":"public","publication":"14th IEEE International Workshop on Factory Communication Systems (WFCS)","publication_identifier":{"eisbn":["978-1-5386-1066-4"]},"date_created":"2021-01-08T08:26:30Z","date_updated":"2023-03-15T13:49:52Z","oa":"1","keyword":["Maintenance engineering","Adaptation models","Machine learning","Data models","Standards","Software","Bayes methods"],"language":[{"iso":"eng"}]},{"intvolume":" 10410","user_id":"15514","type":"conference","_id":"4254","place":"Cham","publication_status":"published","series_title":"Lecture Notes in Computer Science ","publisher":"Springer","abstract":[{"lang":"eng","text":"The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations."}],"conference":{"start_date":"2017-08-29","end_date":"2017-09-01","name":"International Cross-Domain Conference, CD-MAKE 2017","location":"Reggio, Italy"},"department":[{"_id":"DEP5023"}],"editor":[{"last_name":"Holzinger","first_name":"Andreas","full_name":"Holzinger, Andreas"}],"date_updated":"2023-03-15T13:49:51Z","date_created":"2020-12-10T13:40:04Z","publication_identifier":{"eisbn":["9783319668086 "],"isbn":["978-3-319-66807-9"]},"publication":" Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings","language":[{"iso":"eng"}],"keyword":["Alarm flood reduction","Machine learning","Assistive system"],"oa":"1","volume":10410,"citation":{"din1505-2-1":"Büttner, Sebastian ; Wunderlich, Paul ; Heinz, Mario ; Niggemann, Oliver ; Röcker, Carsten: Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In: Holzinger, A. (Hrsg.): Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, Lecture Notes in Computer Science . Bd. 10410. Cham : Springer, 2017, S. 69–82","chicago-de":"Büttner, Sebastian, Paul Wunderlich, Mario Heinz, Oliver Niggemann und Carsten Röcker. 2017. Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In: Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, hg. von Andreas Holzinger, 10410:69–82. Lecture Notes in Computer Science . Cham: Springer.","short":"S. Büttner, P. Wunderlich, M. Heinz, O. Niggemann, C. Röcker, in: A. Holzinger (Ed.), Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, Springer, Cham, 2017, pp. 69–82.","ufg":"Büttner, Sebastian et. al. (2017): Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction, in: Andreas Holzinger (Hg.): Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings (=Lecture Notes in Computer Science 10410), Cham, S. 69–82.","havard":"S. Büttner, P. Wunderlich, M. Heinz, O. Niggemann, C. Röcker, Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction, in: A. Holzinger (Ed.), Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, Springer, Cham, 2017: pp. 69–82.","mla":"Büttner, Sebastian, et al. “Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction.” Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, edited by Andreas Holzinger, vol. 10410, Springer, 2017, pp. 69–82.","bjps":"Büttner S et al. (2017) Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In Holzinger A (ed.), Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, vol. 10410. Cham: Springer, pp. 69–82.","van":"Büttner S, Wunderlich P, Heinz M, Niggemann O, Röcker C. Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In: Holzinger A, editor. Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 84, 89, 129 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings. Cham: Springer; 2017. p. 69–82. (Lecture Notes in Computer Science ; vol. 10410).","chicago":"Büttner, Sebastian, Paul Wunderlich, Mario Heinz, Oliver Niggemann, and Carsten Röcker. “Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction.” In Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, edited by Andreas Holzinger, 10410:69–82. Lecture Notes in Computer Science . Cham: Springer, 2017.","apa":"Büttner, S., Wunderlich, P., Heinz, M., Niggemann, O., & Röcker, C. (2017). Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In A. Holzinger (Ed.), Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings (Vol. 10410, pp. 69–82). Cham: Springer.","ama":"Büttner S, Wunderlich P, Heinz M, Niggemann O, Röcker C. Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction. In: Holzinger A, ed. Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings. Vol 10410. Lecture Notes in Computer Science . Cham: Springer; 2017:69-82.","ieee":"S. Büttner, P. Wunderlich, M. Heinz, O. Niggemann, and C. Röcker, “Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction,” in Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings, Reggio, Italy, 2017, vol. 10410, pp. 69–82."},"title":"Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction","page":"69-82","status":"public","author":[{"id":"61868","full_name":"Büttner, Sebastian","last_name":"Büttner","first_name":"Sebastian"},{"last_name":"Wunderlich","full_name":"Wunderlich, Paul","id":"52317","first_name":"Paul"},{"last_name":"Heinz","first_name":"Mario","full_name":"Heinz, Mario","id":"68913"},{"full_name":"Niggemann, Oliver","id":"10876","last_name":"Niggemann","first_name":"Oliver"},{"id":"61525","first_name":"Carsten","last_name":"Röcker","full_name":"Röcker, Carsten"}],"main_file_link":[{"open_access":"1"}],"year":2017},{"title":"Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning","citation":{"ufg":"Robert, Sebastian et. al. (2016): Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning, in: Andreas Holzinger (Hg.): Machine Learning for Health Informatics : State-of-the-Art and Future Challenges (=Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence 9605), Cham, CH, S. 357–376.","havard":"S. Robert, S. Büttner, C. Röcker, A. Holzinger, Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning, in: A. Holzinger (Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , Springer, Cham, CH, 2016: pp. 357–376.","mla":"Robert, Sebastian, et al. “Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.” Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , edited by Andreas Holzinger, vol. 9605, Springer, 2016, pp. 357–76, doi:10.1007/978-3-319-50478-0_18.","bjps":"Robert S et al. (2016) Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In Holzinger A (ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , vol. 9605. Cham, CH: Springer, pp. 357–376.","van":"Robert S, Büttner S, Röcker C, Holzinger A. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Holzinger A, editor. Machine Learning for Health Informatics : State-of-the-Art and Future Challenges . Cham, CH: Springer; 2016. p. 357–76. (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence ; vol. 9605).","chicago":"Robert, Sebastian, Sebastian Büttner, Carsten Röcker, and Andreas Holzinger. “Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.” In Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , edited by Andreas Holzinger, 9605:357–76. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Cham, CH: Springer, 2016. https://doi.org/10.1007/978-3-319-50478-0_18.","apa":"Robert, S., Büttner, S., Röcker, C., & Holzinger, A. (2016). Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In A. Holzinger (Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges (Vol. 9605, pp. 357–376). Cham, CH: Springer. https://doi.org/10.1007/978-3-319-50478-0_18","din1505-2-1":"Robert, Sebastian ; Büttner, Sebastian ; Röcker, Carsten ; Holzinger, Andreas: Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Holzinger, A. (Hrsg.): Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Bd. 9605. Cham, CH : Springer, 2016, S. 357–376","chicago-de":"Robert, Sebastian, Sebastian Büttner, Carsten Röcker und Andreas Holzinger. 2016. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , hg. von Andreas Holzinger, 9605:357–376. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Cham, CH: Springer. doi:10.1007/978-3-319-50478-0_18, .","short":"S. Robert, S. Büttner, C. Röcker, A. Holzinger, in: A. Holzinger (Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , Springer, Cham, CH, 2016, pp. 357–376.","ama":"Robert S, Büttner S, Röcker C, Holzinger A. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Holzinger A, ed. Machine Learning for Health Informatics : State-of-the-Art and Future Challenges . Vol 9605. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Cham, CH: Springer; 2016:357-376. doi:10.1007/978-3-319-50478-0_18","ieee":"S. Robert, S. Büttner, C. Röcker, and A. Holzinger, “Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning,” in Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , vol. 9605, A. Holzinger, Ed. Cham, CH: Springer, 2016, pp. 357–376."},"volume":9605,"author":[{"last_name":"Robert","first_name":"Sebastian","full_name":"Robert, Sebastian"},{"full_name":"Büttner, Sebastian","id":"61868","last_name":"Büttner","first_name":"Sebastian"},{"first_name":"Carsten","id":"61525","last_name":"Röcker","full_name":"Röcker, Carsten"},{"first_name":"Andreas","last_name":"Holzinger","full_name":"Holzinger, Andreas"}],"year":2016,"status":"public","page":"357-376","publication_identifier":{"isbn":["978-3-319-50477-3 "],"eisbn":["978-3-319-50478-0 "]},"publication":"Machine Learning for Health Informatics : State-of-the-Art and Future Challenges ","date_created":"2020-12-22T14:11:00Z","date_updated":"2023-03-15T13:49:51Z","editor":[{"first_name":"Andreas","last_name":"Holzinger","full_name":"Holzinger, Andreas"}],"keyword":["Decision making","Reasoning","Interactive machine learning","Collaborative interactive machine learning"],"language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence ","publication_status":"published","department":[{"_id":"DEP5023"}],"abstract":[{"lang":"eng","text":"In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale."}],"publisher":"Springer","type":"book_chapter","user_id":"15514","intvolume":" 9605","place":"Cham, CH","_id":"4298","doi":"10.1007/978-3-319-50478-0_18"},{"keyword":["HCI","ambient assisted living","big data","computational intelligence","context awareness","data centric medicine","decision support","interactive data mining","keyword detection","knoweldge bases","knoweldge discovery","machine learning","medical decision support","medical informatics","natural language processing","pervasive health","smart home","ubiquitous computing","visualization","wearable sensors"],"language":[{"iso":"eng"}],"oa":"1","date_updated":"2023-03-15T13:49:52Z","editor":[{"last_name":"Holzinger","full_name":"Holzinger, Andreas","first_name":"Andreas"},{"id":"61525","last_name":"Röcker","full_name":"Röcker, Carsten","first_name":"Carsten"},{"last_name":"Ziefle","first_name":"Martina","full_name":"Ziefle, Martina"}],"publication_identifier":{"isbn":["978-3-319-16225-6"],"eissn":["1611-3349"],"issn":["0302-9743"],"eisbn":["978-3-319-16226-3"]},"date_created":"2021-01-08T12:03:52Z","status":"public","page":"275","main_file_link":[{"open_access":"1","url":"http://www.springerlink.com/content/978-3-319-16226-3 "}],"year":2015,"volume":8700,"citation":{"chicago":"Holzinger, Andreas, Carsten Röcker, and Martina Ziefle, eds. Smart Health: Open Problems and Future Challenges. Vol. 8700. Lecture Notes in Computer Science / Information Systems and Applications, Incl. Internet/Web, and HCI. Heidelberg: Springer, 2015. https://doi.org/10.1007/978-3-319-16226-3.","apa":"Holzinger, A., Röcker, C., & Ziefle, M. (Eds.). (2015). Smart Health: Open Problems and Future Challenges (Vol. 8700). Heidelberg: Springer. https://doi.org/10.1007/978-3-319-16226-3","bjps":"Holzinger A, Röcker C and Ziefle M (eds) (2015) Smart Health: Open Problems and Future Challenges. Heidelberg: Springer.","havard":"A. Holzinger, C. Röcker, M. Ziefle, eds., Smart Health: Open Problems and Future Challenges, Springer, Heidelberg, 2015.","mla":"Holzinger, Andreas, et al., editors. Smart Health: Open Problems and Future Challenges. Vol. 8700, Springer, 2015, doi:10.1007/978-3-319-16226-3.","ufg":"Holzinger, Andreas et. al. (Hgg.) (2015): Smart Health: Open Problems and Future Challenges (=Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI 8700), Heidelberg.","van":"Holzinger A, Röcker C, Ziefle M, editors. Smart Health: Open Problems and Future Challenges. Heidelberg: Springer; 2015. 275 p. (Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI; vol. 8700).","chicago-de":"Holzinger, Andreas, Carsten Röcker und Martina Ziefle, Hrsg. 2015. Smart Health: Open Problems and Future Challenges. Bd. 8700. Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI. Heidelberg: Springer. doi:10.1007/978-3-319-16226-3, .","din1505-2-1":"Holzinger, A. ; Röcker, C. ; Ziefle, M. (Hrsg.): Smart Health: Open Problems and Future Challenges, Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI. Bd. 8700. Heidelberg : Springer, 2015","short":"A. Holzinger, C. Röcker, M. Ziefle, eds., Smart Health: Open Problems and Future Challenges, Springer, Heidelberg, 2015.","ieee":"A. Holzinger, C. Röcker, and M. Ziefle, Eds., Smart Health: Open Problems and Future Challenges, vol. 8700. Heidelberg: Springer, 2015.","ama":"Holzinger A, Röcker C, Ziefle M, eds. Smart Health: Open Problems and Future Challenges. Vol 8700. Heidelberg: Springer; 2015. doi:10.1007/978-3-319-16226-3"},"title":"Smart Health: Open Problems and Future Challenges","place":"Heidelberg","_id":"4336","doi":"10.1007/978-3-319-16226-3","user_id":"15514","intvolume":" 8700","type":"book_editor","publisher":"Springer","department":[{"_id":"DEP5023"}],"abstract":[{"text":"Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems.\r\n\r\nThe very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning.\r\n\r\nThe papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.","lang":"eng"}],"publication_status":"published","series_title":"Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI"},{"date_created":"2019-12-04T12:43:12Z","type":"conference","publication":"Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)","date_updated":"2023-03-15T13:49:39Z","user_id":"68554","oa":"1","_id":"2167","language":[{"iso":"eng"}],"place":"Austin, Texas, USA","keyword":["Cyber-Physical Systems","Machine Learning","Diagnosis","Anomaly Detection"],"title":"On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda","citation":{"van":"Niggemann O, Lohweg V. On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA; 2015.","ama":"Niggemann O, Lohweg V. On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA; 2015.","ufg":"Niggemann, Oliver/Lohweg, Volker (2015): On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda, in: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15), Austin, Texas, USA.","mla":"Niggemann, Oliver, and Volker Lohweg. “On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda.” Twenty-Ninth Conference on Artificial Intelligence (AAAI-15), 2015.","havard":"O. Niggemann, V. Lohweg, On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda, in: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15), Austin, Texas, USA, 2015.","bjps":"Niggemann O and Lohweg V (2015) On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA.","apa":"Niggemann, O., & Lohweg, V. (2015). On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA.","ieee":"O. Niggemann and V. Lohweg, “On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda,” in Twenty-Ninth Conference on Artificial Intelligence (AAAI-15), 2015.","chicago":"Niggemann, Oliver, and Volker Lohweg. “On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda.” In Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA, 2015.","short":"O. Niggemann, V. Lohweg, in: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15), Austin, Texas, USA, 2015.","din1505-2-1":"Niggemann, Oliver ; Lohweg, Volker: On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA, 2015","chicago-de":"Niggemann, Oliver und Volker Lohweg. 2015. On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda. In: Twenty-Ninth Conference on Artificial Intelligence (AAAI-15). Austin, Texas, USA."},"abstract":[{"text":"Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.\r\n","lang":"eng"}],"department":[{"_id":"DEP5023"}],"author":[{"first_name":"Oliver","last_name":"Niggemann","id":"10876","full_name":"Niggemann, Oliver"},{"orcid":"0000-0002-3325-7887","id":"1804","last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker"}],"year":2015,"main_file_link":[{"url":"https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9530/9691","open_access":"1"}],"status":"public"},{"publication":"IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems","type":"conference","date_created":"2019-12-02T08:15:18Z","user_id":"45673","date_updated":"2023-03-15T13:49:38Z","oa":"1","keyword":["Fuzzy Logic","Probability Theory","Fuzzy-Pattern-Classification","Machine Learning","Artificial Intelligence","Pattern Recognition"],"language":[{"iso":"eng"}],"_id":"2087","title":"Fuzzy-Pattern-Classifier Training with Small Data Sets","citation":{"apa":"Mönks, U., Lohweg, V., & Petker, D. (2010). Fuzzy-Pattern-Classifier Training with Small Data Sets. In IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July 2010, Dortmund, Germany.","chicago":"Mönks, Uwe, Volker Lohweg, and Denis Petker. “Fuzzy-Pattern-Classifier Training with Small Data Sets.” In IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010.","van":"Mönks U, Lohweg V, Petker D. Fuzzy-Pattern-Classifier Training with Small Data Sets. In: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July 2010, Dortmund, Germany; 2010.","mla":"Mönks, Uwe, et al. “Fuzzy-Pattern-Classifier Training with Small Data Sets.” IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems, 28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010.","bjps":"Mönks U, Lohweg V and Petker D (2010) Fuzzy-Pattern-Classifier Training with Small Data Sets. IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July 2010, Dortmund, Germany.","havard":"U. Mönks, V. Lohweg, D. Petker, Fuzzy-Pattern-Classifier Training with Small Data Sets, in: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems, 28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010.","ufg":"Mönks, Uwe et. al. (2010): Fuzzy-Pattern-Classifier Training with Small Data Sets, in: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems.","short":"U. Mönks, V. Lohweg, D. Petker, in: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems, 28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010.","chicago-de":"Mönks, Uwe, Volker Lohweg und Denis Petker. 2010. Fuzzy-Pattern-Classifier Training with Small Data Sets. In: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July 2010, Dortmund, Germany.","din1505-2-1":"Mönks, Uwe ; Lohweg, Volker ; Petker, Denis: Fuzzy-Pattern-Classifier Training with Small Data Sets. In: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems : 28 Jun 2010 - 02 July 2010, Dortmund, Germany, 2010","ieee":"U. Mönks, V. Lohweg, and D. Petker, “Fuzzy-Pattern-Classifier Training with Small Data Sets,” in IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems, 2010.","ama":"Mönks U, Lohweg V, Petker D. Fuzzy-Pattern-Classifier Training with Small Data Sets. In: IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems. 28 Jun 2010 - 02 July 2010, Dortmund, Germany; 2010."},"publication_status":"published","year":2010,"author":[{"first_name":"Uwe","full_name":"Mönks, Uwe","last_name":"Mönks","id":"1825"},{"full_name":"Lohweg, Volker","first_name":"Volker","last_name":"Lohweg","id":"1804","orcid":"0000-0002-3325-7887"},{"full_name":"Petker, Denis","first_name":"Denis","last_name":"Petker"}],"main_file_link":[{"open_access":"1","url":"https://www.th-owl.de/init/uploads/tx_initdb/00800426_01.pdf"}],"department":[{"_id":"DEP5023"}],"abstract":[{"lang":"eng","text":"It is likely in real-world applications that only little data isavailable for training a knowledge-based system. We present a method forautomatically training the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification system that works also when only littledata is available and the universal set is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s member-ship functions are trained using probability distribution functions."}],"status":"public","publisher":"28 Jun 2010 - 02 July 2010, Dortmund, Germany"}]